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Federici, M., Forré, P. D., Tomioka, R., & Veeling, B. S. (in press). Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck. In ICLR 2024: 12th International Conference on Learning Representations https://doi.org/10.48550/arXiv.2309.07200
2023
Arts, M., García Satorras, V., Huang, C.-W., Zügner, D., Federici, M., Clementi, C., Noé, F., Pinsler, R., & van den Berg, R. (2023). Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics. Journal of Chemical Theory and Computation, 19(18), 6151-6159. https://doi.org/10.1021/acs.jctc.3c00702[details]
Wang, Q., Federici, M., & van Hoof, H. C. (2023). Bridge the Inference Gaps of Neural Processes via Expectation Maximization. In International Conference on Learning Representations https://openreview.net/forum?id=A7v2DqLjZdq
2022
Alaniz, S., Federici, M., & Akata, Z. (2022). Compositional Mixture Representations for Vision and Text. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops: Proceedings : New Orleans, Louisiana, 19-24 June 2022 (pp. 4201-4210). (CVPRW; Vol. 2022). IEEE Computer Society. https://doi.org/10.48550/arXiv.2206.06404, https://doi.org/10.1109/CVPRW56347.2022.00465[details]
Mourdoukoutas, N., Federici, M., Pantalos, G., van der Wilk, M., & Fortuin, V. (2021). A Bayesian Approach to Invariant Deep Neural Networks. In ICML 2021 Workshop on Uncertainty and Robustness in Deep Learning https://arxiv.org/pdf/2107.09301.pdf
Zuiderveld, J., Federici, M., & Bekkers, E. J. (2021). Towards lightweight controllable audio synthesis with conditional implicit neural representations. In Workshop on Deep Generative Models and Downstream Applications at NeurIPS 2021 https://arxiv.org/pdf/2111.08462.pdf
Federici, M., Forre, P., & Tomioka, R. (2022). An Information-theoretic Approach to Distribution Shifts. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P. S. Liang, & J. Wortman Vaughan (Eds.), 35th Conference on Neural Information Processing Systems (NeurIPS 2021) : online, 6-14 December 2021 (Vol. 21, pp. 17628-17641). (Advances in Neural Information Processing Systems; Vol. 34). Neural Information Processing Systems Foundation. https://doi.org/10.48550/arXiv.2106.03783[details]
Miller, B. K., Federici, M., Weniger, C., & Forré, P. D. (2023). Simulation-based Inference with the Generalized Kullback-Leibler Divergence. Paper presented at ICML 2023 Workshop: Synergy of Scientific and Machine Learning Modeling, Honolulu, Hawaii, United States. https://arxiv.org/abs/2310.01808
2020
Federici, M., Dutta, A., Forré, P., Kushman, N., & Akata, Z. (2020). Learning Robust Representations via Multi-View Information Bottleneck. Paper presented at 8th International Conference on Learning Representations, Addis Abeba, Ethiopia. https://openreview.net/pdf?id=B1xwcyHFDr
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